计算机工程与应用2017，Vol.53Issue(19)：204-210,7.DOI:10.3778/j.issn.1002-8331.1605-0049
基于1-norm SVM权值学习的多示例目标跟踪
Multiple instance object tracking algorithm based on 1-norm SVM weight distribution
詹金珍 1滑维鑫 2乔芸3
作者信息
- 1. 西北工业大学 明德学院,西安 710124
- 2. 西北工业大学 自动化学院,西安 710072
- 3. 中国移动通信集团 陕西有限公司,西安 710074
- 折叠
摘要
Abstract
For the poor robustness and target drift problem of the most existing tracking algorithms in complex environ-ment, an improved target tracking algorithm based on multiple instance learning is proposed. The MIL tracker ignores the differences of each sample in the process of computing the bag probability, which declines the performance of classifier, and there exists complex problem in choosing the weak classifier. This paper solves these problems by computing the importance of each sample to bag probability based on the 1-norm SVM method. Then, it adopts inner product method to compute the log-likelihood of bag in the process of choose weak classifier, which is benefit to reduce the computing complexity. Experimental results show that the proposed algorithm performs well with strong robustness and high tracking accuracy under the complicated environments such as occlusion, rotation, pose and illumination change.关键词
多示例学习/1-normSVM/分类器/目标跟踪Key words
multiple instance learning/1-norm SVM/classifier/object tracking分类
信息技术与安全科学引用本文复制引用
詹金珍,滑维鑫,乔芸..基于1-norm SVM权值学习的多示例目标跟踪[J].计算机工程与应用,2017,53(19):204-210,7.
